Data is the key to transforming any business. Accelerated by COVID-19, businesses have started to become more data-driven and embrace digital transformation strategies. Failure to understand data, what a business has, and what a business needs, is compromising far too many digital transformation plans, and leading businesses to waste years on projects that ultimately, will never deliver.
Peter Ruffley, CEO, Zizo believes that by going through the data lifecycle to understand what data a business has today, valuable insight can be immediately leveraged, and can be built on to drive the digital transformation process.
Digital Transformation Paralysis
One of the biggest issues facing companies of all sizes is a complete lack of knowledge, or honesty, about current data resources. The quality of data that an organisation can function on is much lower than the standard required for digital transformation.
By understanding the existing data resources first, organisations can then drive effective change and unlock immediate value, only then will they be able to explore the real opportunities they have to meet needs and realise ambitions. Businesses need to get the foundations right, have the right quality of data, and it being available at the right time.
It can appear simple to collect data but, as far too many companies have discovered, there is a huge difference between any data and the right data.
Without collecting the right, usable data from the outset, businesses risk compromising the entire data lifecycle, and derailing digital transformation initiatives as a result. Robust data collection processes look closely at the ‘how, where, and what’ to ensure the correct data is in place and use expert data validation to determine the quality of data before moving to the next stage of the data lifecycle.
Organisations of all sizes are often data-rich, but insight-poor: there is a huge gap between creating an extensive data resource and unlocking real business value. Single sources of information can be interesting, but the true business picture can only be revealed by combining multiple data sources.
What information is required by the business? Which data sources can be combined to reveal vital business insights? And what is the best approach to combining data to ensure the right information is produced? Combining data is a complex process.
Data may have intrinsic value, but its only true value to the business is the information it provides. Therefore, contextualisation is crucial to create this information and deliver actionable insights, in turn, enabling intelligent decision-making.
Working with an independent data expert can help businesses to understand their data, and by applying algorithms derived from Machine Learning and Artificial Intelligence to produce insights, organisations can derive value from the data more quickly and benefit from the insights produced.
The most critical aspect of the data lifecycle (collect, combine, context, change) is to remember that it is a ‘cycle,’ and not a finite process. While businesses undertake each of these stages, changes may occur or need to take place, to make the cycle, and end results, more effective.
For example, if the business requires more data to understand how a particular operation is achieved, changes need to be made in the ‘data collection’ stage. It is important to remain agile and flexible throughout the process, learning from business findings in each stage, and identifying the business areas that need improvement. This is a continually evolving cycle, and businesses need to repeat and change where necessary.